Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"
# 
# df <- path %>% 
#   paste0("latest.json") %>% 
#   readr::read_lines() %>% 
#   paste0(path, .) %>% 
#   jsonlite::fromJSON()

df <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/latest.json" %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

# path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"
# 
# df_s <- path %>% 
#   paste0("latest.json") %>% 
#   readr::read_lines() %>% 
#   paste0(path, .) %>% 
#   jsonlite::fromJSON()

df_s <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/latest.json" %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$confirmed[1]

## [1] 87242

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 95634

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-12-15T22:43:51+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

病床データ

新型コロナウイルス対策病床オープンデータのデータも用意しておく。

if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>% 
  googlesheets4::read_sheet() %>% 
  dplyr::arrange(dplyr::desc(`発表日`)) %>% 
  dplyr::distinct(`自治体名`, .keep_all = TRUE) %>% 
  dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
                date = `発表日`) %>% 
  dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}

　

対策ダッシュボード

NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。

"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>% 
  readr::read_csv(guess_max = 12500) %>% 
  dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
                confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
                `入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
                `確認中`) %>% 
  dplyr::mutate(date = lubridate::as_date(date)) %>% 
  dplyr::mutate_if(is.numeric, .funs = as.integer)

　

新型コロナ関連ニュース

新型コロナ関連のニュース

news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>% 
    readr::read_csv() %>% 
  dplyr::filter(area == "日本")
news

　

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	187212
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	184712	0
dateAnnounced	0	1.00	10	10	0	322	0
gender	76492	0.59	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	182462	0.03	8	23	0	8	0
notes	102931	0.45	1	270	0	81300	1
mhlwPatientNumber	186763	0.00	1	11	0	434	0
prefecturePatientNumber	70531	0.62	5	20	0	116672	0
prefectureSourceURL	155812	0.17	5	224	0	3455	0
residence	84582	0.55	1	38	0	1429	0
sourceURL	1250	0.99	1	239	0	9757	0
relatedPatients	174625	0.07	2	259	0	7470	0
knownCluster	184678	0.01	3	88	0	235	0
detectedCityTown	158179	0.16	2	22	0	667	0
cityPrefectureNumber	158480	0.15	1	34	0	28723	2
citySourceURL	174979	0.07	7	317	0	3695	0
deceasedDate	184621	0.01	10	10	0	272	0
deceasedReportedDate	185988	0.01	10	62	0	208	0
deathSourceURL	186133	0.01	14	123	0	658	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 184711, FAL: 2501
charterFlightPassenger	187198	0	1.00	TRU: 14
cruisePassengerDisembarked	187201	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	22.05	24.92	-1	-1	20	40	100	▇▅▃▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`, -pref) %>% 
  dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>% 
  tidyr::drop_na(pref)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	182995
Number of columns	18
_______________________
Column type frequency:
character	2
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	2	8	0	182995	0
knownCluster	180511	0.01	3	88	0	232	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-12-15	2020-10-17	319
deceasedDate	182616	0	2020-02-13	2020-11-19	2020-05-08	150
deceasedReportedDate	182666	0	2020-02-13	2020-10-17	2020-05-16	130

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	73880	0.60	FALSE	2	M: 61012, F: 48103
patientStatus	180484	0.01	FALSE	8	Hos: 1246, Dec: 371, Hom: 315, Dis: 276
ageBracket	0	1.00	FALSE	13	-1: 73965, 20: 29297, 30: 18917, 40: 15988
pcode	0	1.00	FALSE	47	13: 48039, 27: 25421, 14: 15428, 23: 13049
pref	0	1.00	FALSE	47	東京都: 48039, 大阪府: 25421, 神奈川: 15428, 愛知県: 13049
region	0	1.00	FALSE	8	関東地: 87242, 近畿地: 40396, 中部地: 20422, 九州地: 15640
広域圏	16403	0.91	FALSE	8	首都圏: 87701, 近畿圏: 39311, 中部圏: 18901, 九州圏: 10790
通俗的区分	0	1.00	FALSE	11	関東: 87242, 関西: 39311, 東海: 17911, 北海道: 11553
fct_pref	0	1.00	FALSE	47	Tok: 48039, Osa: 25421, Kan: 15428, Aic: 13049

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 182995
charterFlightPassenger	182988	0	1.00	TRU: 7
cruisePassengerDisembarked	182984	0	1.00	TRU: 11
cluster	0	1	0.01	FAL: 180511, TRU: 2484

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

Data Wrangling

陽性者の集計

最初に陽性者をキーに集計する。 　

全国集計

全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

r_by_all <- x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_all %>% 
  dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

地方別集計

次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_region %>% 
  dplyr::rename(`地方` = region,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate)

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.45）。

r_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

都道府県別集計

同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。

r_by_pref <- x %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  dplyr::select(pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))

r_by_pref %>% 
  dplyr::rename(`都道府県` = pref,
                `累計陽性者数[人]` = n, `推計人口[千人]` = population,
                `人口千人あたりの累計陽性者数` = rate) %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.45）。

r_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。

r_by_pref %>% 
  dplyr::filter(population < 5500) %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
                         colour = "gray", linetype = "dashed") + 
    ggplot2::geom_point(ggplot2::aes(colour = key)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数[人]")

　

クラスタ比率

全国のクラスタ比率

x %>% 
  dplyr::filter(!is.na(pref)) %>% 
  dplyr::group_by(cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

地方別クラスタ比率

地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  dplyr::rename(`地方` = region,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio)

　

都道府県別クラスタ比率

同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。

x %>% 
  dplyr::group_by(pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  dplyr::rename(`都道府県` = pref,
                `非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
                `クラスタ比率[%]` = ratio) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

陽性者の日次集計

　

全国日次集計

全国の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))

x_by_all %>% 
  dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
                `累計陽性者数` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"

# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")

sec_scale <- 100

emergency <- news %>% 
  dplyr::filter(category == "緊急事態")

goto <- news %>% 
  dplyr::filter(category == "GoTo")

x_by_all %>% 
  dplyr::mutate(hday = is.jholiday(target_date = date,
                                   holiday_source = "./Covid19/syukujitsu.csv"),
                wday = lubridate::wday(date, week_start = 1),
                wday = dplyr::if_else(wday > 5, TRUE, FALSE),
                wday = dplyr::if_else(hday | wday, 3000L,  NA_integer_)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
                      fill = "red", alpha = 0.1) + 
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
                        colour = "dark blue", linetype = "dashed", size = 0.15) +
    ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
                        colour = "magenta", linetype = "dashed", size = 0.25) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      fill = "dark gray", alpha = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
                              size = 2.0, data = emergency) +
    ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
                              size = 2.5, data = goto, colour = "magenta") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別日次集計

同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_region <- x %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::arrange(date)

x_by_region %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

x_by_region %>% 
  dplyr::select(`地方` = region,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "陽性者数") 

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "累計陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 4) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

地方単位で可視化。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dotted", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（点線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

sec_scale <- 20
ncol <- 2

x_by_region %>% 
  dplyr::rename(key = region) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "dashed", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol  = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "陽性者累計（実線）")
    )

　

都道府県別日次集計

同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。

x_by_pref <- x %>% 
  dplyr::group_by(date, pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n)),
                ma28 = purrr::map(data, ~ ma28(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::arrange(date)

x_by_pref %>% 
  dplyr::filter(date == max(date)) %>% 
  dplyr::mutate(ma7 = round(ma7, 1)) %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7) %>% 
  DT::datatable()

x_by_pref %>% 
  dplyr::select(`都道府県` = pref,
                `発表日` = date, `陽性者数` = n, `前日差` = diff,
                `陽性者累計` = cum, `移動平均（7日）` = ma7)

　

上表を可視化する。

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

傾向が見えるように縦軸をフリースケールとする。

x_by_pref %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

死亡者の日次集計

厚生労働省のデータと乖離がある。 　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>% 
  dplyr::group_by(pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>% 
  dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_prefs %>% 
  dplyr::rename(key = pref) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
    ggplot2::facet_wrap(~ key, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計陽性者数（折線）")
    )

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_pref, ., by = "pref") %>% 
  dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
   ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
                       linetype = "dashed", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・同移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累計死亡者数（実線）")
    )

　

年代別

x_by_age <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9"))
x_by_age

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(rate = round((n / sum(n) * 100), 1))

　

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::pivot_wider(names_from = ageBracket, values_from = n)

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() +
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) +
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(region, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(region = forcats::fct_rev(region)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = region)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::facet_grid(~ cluster) +
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

都道府県別

g_age_by_pref <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::group_by(pref, ageBracket, cluster) %>%
  # dplyr::group_by(pref, ageBracket) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") +
    # ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref

g_age_by_pref + 
  ggplot2::facet_grid(~ cluster)

g_age_by_pref_wo <- x %>% 
  dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
                ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>% 
  dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket, 
                                                  "100", "90", "80", "70", "60",
                                                  "50", "40", "30", "20", "10",
                                                  "0-9")) %>%
  dplyr::filter(ageBracket != "Unknown") %>% 
  dplyr::group_by(pref, ageBracket, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::mutate(pref = forcats::fct_rev(pref)) %>% 
  ggplot2::ggplot(ggplot2::aes(x = pref)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
                      position = "fill") + 
    ggplot2::scale_fill_brewer(palette = "Set3") + 
    ggplot2::scale_y_continuous(labels = scales::percent) + 
    ggplot2::coord_flip() + 
    ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
                        colour = "dark gray") + 
    ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "")

g_age_by_pref_wo

g_age_by_pref_wo + 
  ggplot2::facet_grid(~ cluster) + 
  ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))

　

Visualize

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
    ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_pref %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_pref %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ymax <- 3000

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, ymax), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, ymax), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, .y) {
                plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 128.53783 142.62670 124.65030 104.39146 107.68849  99.45212  82.36473
##  [8]  83.26270  96.35975  94.60007  83.57336  74.97711  82.16650  73.19390
## 
## $北海道地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%        95%
## 49.00000 113.04618 104.845397
## 49.14286 124.48609 114.883024
## 49.28571 101.58376  89.373073
## 49.42857  79.94340  67.001380
## 49.57143  80.34410  65.868854
## 49.71429  69.65825  53.886327
## 49.85714  50.27631  33.289719
## 50.00000  46.86778  27.601475
## 50.14286  56.49494  35.391788
## 50.28571  50.43316  27.052605
## 50.42857  36.89831  12.190035
## 50.57143  25.55905  -0.601294
## 50.71429  29.76956   2.032299
## 50.85714  17.87816 -11.404223
## 
## $北海道地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 144.0295 152.2303
## 49.14286 160.7673 170.3704
## 49.28571 147.7168 159.9275
## 49.42857 128.8395 141.7815
## 49.57143 135.0329 149.5081
## 49.71429 129.2460 145.0179
## 49.85714 114.4532 131.4397
## 50.00000 119.6576 138.9239
## 50.14286 136.2246 157.3277
## 50.28571 138.7670 162.1475
## 50.42857 130.2484 154.9567
## 50.57143 124.3952 150.5555
## 50.71429 134.5634 162.3007
## 50.85714 128.5096 157.7920
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1]  96.32581 102.37060 102.26415  98.76660  94.95523  92.74749  92.55282
##  [8]  93.72937  95.30723  96.55004  97.17799  97.29760  97.19237  97.12451
## 
## $東北地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 87.07549 82.17867
## 49.14286 91.95068 86.43470
## 49.28571 91.65498 86.03882
## 49.42857 88.06906 82.40612
## 49.57143 84.01700 78.22665
## 49.71429 81.20639 75.09690
## 49.85714 80.07127 73.46394
## 50.00000 80.22040 73.06919
## 50.14286 80.89546 73.26633
## 50.28571 81.41793 73.40747
## 50.42857 81.46503 73.14709
## 50.57143 81.07505 72.48735
## 50.71429 80.47742 71.62905
## 50.85714 79.90581 70.79079
## 
## $東北地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 105.5761 110.4729
## 49.14286 112.7905 118.3065
## 49.28571 112.8733 118.4895
## 49.42857 109.4641 115.1271
## 49.57143 105.8935 111.6838
## 49.71429 104.2886 110.3981
## 49.85714 105.0344 111.6417
## 50.00000 107.2383 114.3895
## 50.14286 109.7190 117.3481
## 50.28571 111.6821 119.6926
## 50.42857 112.8910 121.2089
## 50.57143 113.5201 122.1078
## 50.71429 113.9073 122.7557
## 50.85714 114.3432 123.4582
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 1285.4704 1375.7892 1373.2949 1414.8349 1169.1514  929.1773 1175.2846
##  [8] 1414.3068 1509.3520 1507.0616 1545.8458 1296.3433 1053.0246 1297.1030
## 
## $関東地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 1206.7878 1165.1358
## 49.14286 1277.9249 1226.1187
## 49.28571 1267.6324 1211.6980
## 49.42857 1305.4208 1247.5005
## 49.57143 1056.9471  997.5497
## 49.71429  813.6621  752.5120
## 49.85714 1055.0947  991.4700
## 50.00000 1278.3059 1206.3113
## 50.14286 1360.0277 1280.9802
## 50.28571 1346.8849 1262.0925
## 50.42857 1376.7227 1287.1944
## 50.57143 1119.4868 1025.8646
## 50.71429  869.0527  771.6639
## 50.85714 1106.2222 1005.1761
## 
## $関東地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 1364.153 1405.805
## 49.14286 1473.654 1525.460
## 49.28571 1478.957 1534.892
## 49.42857 1524.249 1582.169
## 49.57143 1281.356 1340.753
## 49.71429 1044.693 1105.843
## 49.85714 1295.474 1359.099
## 50.00000 1550.308 1622.302
## 50.14286 1658.676 1737.724
## 50.28571 1667.238 1752.031
## 50.42857 1714.969 1804.497
## 50.57143 1473.200 1566.822
## 50.71429 1236.996 1334.385
## 50.85714 1487.984 1589.030
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 354.6739 372.8354 333.7853 370.0566 299.1800 230.9966 330.9151 386.6591
##  [9] 403.7116 362.8059 394.8268 319.3431 247.3498 345.2349
## 
## $中部地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 329.5136 316.1945
## 49.14286 342.9148 327.0758
## 49.28571 300.0478 282.1882
## 49.42857 333.8073 314.6182
## 49.57143 260.8600 240.5747
## 49.71429 190.6827 169.3419
## 49.85714 288.3819 265.8661
## 50.00000 336.7711 310.3621
## 50.14286 348.4677 319.2234
## 50.28571 302.3210 270.3023
## 50.42857 329.7165 295.2492
## 50.57143 250.1191 213.4742
## 50.71429 174.4838 135.9108
## 50.85714 269.0850 228.7737
## 
## $中部地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 379.8342 393.1533
## 49.14286 402.7560 418.5951
## 49.28571 367.5228 385.3823
## 49.42857 406.3058 425.4949
## 49.57143 337.4999 357.7852
## 49.71429 271.3104 292.6513
## 49.85714 373.4484 395.9641
## 50.00000 436.5470 462.9561
## 50.14286 458.9554 488.1998
## 50.28571 423.2907 455.3094
## 50.42857 459.9371 494.4044
## 50.57143 388.5672 425.2121
## 50.71429 320.2158 358.7887
## 50.85714 421.3848 461.6961
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 717.9376 694.2680 625.4729 719.1314 580.9560 406.8153 577.3901 736.7745
##  [9] 723.7836 654.9886 748.6470 610.4717 436.3309 606.9057
## 
## $近畿地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 669.6129 644.0313
## 49.14286 636.5250 605.9577
## 49.28571 564.5647 532.3219
## 49.42857 655.2144 621.3789
## 49.57143 514.1658 478.8092
## 49.71429 337.2704 300.4556
## 49.85714 505.1955 466.9780
## 50.00000 652.1094 607.2903
## 50.14286 631.9261 583.2997
## 50.28571 558.5578 507.5104
## 50.42857 647.8502 594.4916
## 50.57143 505.4903 449.9166
## 50.71429 327.3256 269.6216
## 50.85714 494.0196 434.2613
## 
## $近畿地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 766.2624 791.8440
## 49.14286 752.0109 782.5782
## 49.28571 686.3812 718.6240
## 49.42857 783.0483 816.8838
## 49.57143 647.7463 683.1029
## 49.71429 476.3602 513.1751
## 49.85714 649.5846 687.8021
## 50.00000 821.4397 866.2588
## 50.14286 815.6411 864.2675
## 50.28571 751.4193 802.4667
## 50.42857 849.4437 902.8023
## 50.57143 715.4530 771.0267
## 50.71429 545.3363 603.0403
## 50.85714 719.7917 779.5500
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 113.5417 113.8808 114.2199 114.5590 114.8981 115.2372 115.5762 115.9153
##  [9] 116.2544 116.5935 116.9326 117.2717 117.6108 117.9498
## 
## $中国地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%      95%
## 49.00000 102.31705 96.37506
## 49.14286 101.44786 94.86625
## 49.28571 100.68611 93.52176
## 49.42857 100.00741 92.30428
## 49.57143  99.39539 91.18876
## 49.71429  98.83843 90.15747
## 49.85714  98.32796 89.19728
## 50.00000  97.85742 88.29814
## 50.14286  97.42165 87.45219
## 50.28571  97.01653 86.65310
## 50.42857  96.63867 85.89573
## 50.57143  96.28530 85.17579
## 50.71429  95.95406 84.48969
## 50.85714  95.64295 83.83439
## 
## $中国地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 124.7664 130.7084
## 49.14286 126.3138 132.8954
## 49.28571 127.7537 134.9180
## 49.42857 129.1105 136.8137
## 49.57143 130.4007 138.6074
## 49.71429 131.6359 140.3168
## 49.85714 132.8245 141.9552
## 50.00000 133.9732 143.5325
## 50.14286 135.0872 145.0566
## 50.28571 136.1705 146.5339
## 50.42857 137.2265 147.9694
## 50.57143 138.2580 149.3676
## 50.71429 139.2675 150.7318
## 50.85714 140.2567 152.0653
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 29.45330 26.55329 26.55329 26.55329 26.55329 26.55329 26.55329 26.55329
##  [9] 26.55329 26.55329 26.55329 26.55329 26.55329 26.55329
## 
## $四国地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 24.86185 22.43128
## 49.14286 21.50028 18.82537
## 49.28571 21.28210 18.49169
## 49.42857 21.07259 18.17129
## 49.57143 20.87081 17.86269
## 49.71429 20.67595 17.56468
## 49.85714 20.48735 17.27623
## 50.00000 20.30443 16.99649
## 50.14286 20.12673 16.72471
## 50.28571 19.95380 16.46024
## 50.42857 19.78529 16.20253
## 50.57143 19.62088 15.95108
## 50.71429 19.46027 15.70546
## 50.85714 19.30323 15.46527
## 
## $四国地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 34.04475 36.47532
## 49.14286 31.60630 34.28120
## 49.28571 31.82448 34.61488
## 49.42857 32.03398 34.93529
## 49.57143 32.23577 35.24389
## 49.71429 32.43063 35.54190
## 49.85714 32.61923 35.83034
## 50.00000 32.80214 36.11009
## 50.14286 32.97985 36.38187
## 50.28571 33.15278 36.64634
## 50.42857 33.32129 36.90405
## 50.57143 33.48570 37.15550
## 50.71429 33.64630 37.40112
## 50.85714 33.80335 37.64130
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 204.8221 224.5606 223.4975 211.7114 188.0787 174.8414 192.2536 207.1544
##  [9] 221.5471 223.3442 216.7872 200.4928 190.4988 203.6449
## 
## $九州地方$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 180.9002 168.23670
## 49.14286 194.6492 178.81502
## 49.28571 189.0361 170.79339
## 49.42857 175.8729 156.90109
## 49.57143 150.3720 130.41134
## 49.71429 133.8152 112.09729
## 49.85714 148.1571 124.81378
## 50.00000 157.2801 130.87819
## 50.14286 167.2273 138.47206
## 50.28571 165.0082 134.12695
## 50.42857 155.4036 122.90911
## 50.57143 136.0370 101.91610
## 50.71429 122.7119  86.82774
## 50.85714 132.6832  95.11834
## 
## $九州地方$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 228.7440 241.4075
## 49.14286 254.4721 270.3062
## 49.28571 257.9588 276.2016
## 49.42857 247.5499 266.5217
## 49.57143 225.7853 245.7460
## 49.71429 215.8676 237.5856
## 49.85714 236.3502 259.6935
## 50.00000 257.0288 283.4306
## 50.14286 275.8670 304.6222
## 50.28571 281.6802 312.5614
## 50.42857 278.1708 310.6653
## 50.57143 264.9486 299.0695
## 50.71429 258.2856 294.1698
## 50.85714 274.6066 312.1714

　

都道府県別

x_by_pref %>% 
  dplyr::select(pref, n) %>% 
  split(.$pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 128.53783 142.62670 124.65030 104.39146 107.68849  99.45212  82.36473
##  [8]  83.26270  96.35975  94.60007  83.57336  74.97711  82.16650  73.19390
## 
## $北海道$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%        95%
## 49.00000 113.04618 104.845397
## 49.14286 124.48609 114.883024
## 49.28571 101.58376  89.373073
## 49.42857  79.94340  67.001380
## 49.57143  80.34410  65.868854
## 49.71429  69.65825  53.886327
## 49.85714  50.27631  33.289719
## 50.00000  46.86778  27.601475
## 50.14286  56.49494  35.391788
## 50.28571  50.43316  27.052605
## 50.42857  36.89831  12.190035
## 50.57143  25.55905  -0.601294
## 50.71429  29.76956   2.032299
## 50.85714  17.87816 -11.404223
## 
## $北海道$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 144.0295 152.2303
## 49.14286 160.7673 170.3704
## 49.28571 147.7168 159.9275
## 49.42857 128.8395 141.7815
## 49.57143 135.0329 149.5081
## 49.71429 129.2460 145.0179
## 49.85714 114.4532 131.4397
## 50.00000 119.6576 138.9239
## 50.14286 136.2246 157.3277
## 50.28571 138.7670 162.1475
## 50.42857 130.2484 154.9567
## 50.57143 124.3952 150.5555
## 50.71429 134.5634 162.3007
## 50.85714 128.5096 157.7920
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 6.534009 4.294187 5.979267 4.711534 5.665285 4.947752 5.487572 5.081450
##  [9] 5.386987 5.157123 5.330056 5.199954 5.297833 5.224196
## 
## $青森県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%        95%
## 49.00000 3.8178104  2.3799418
## 49.14286 1.2281536 -0.3949063
## 49.28571 2.8223074  1.1511138
## 49.42857 1.3137834 -0.4848773
## 49.57143 2.1561595  0.2985406
## 49.71429 1.2511186 -0.7057607
## 49.85714 1.6713834 -0.3487849
## 50.00000 1.1073257 -0.9964488
## 50.14286 1.2919564 -0.8758219
## 50.28571 0.9222916 -1.3194928
## 50.42857 0.9763634 -1.3283422
## 50.57143 0.7185818 -1.6537133
## 50.71429 0.7011398 -1.7322029
## 50.85714 0.5085554 -1.9877539
## 
## $青森県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 9.250207 10.688076
## 49.14286 7.360220  8.983280
## 49.28571 9.136227 10.807421
## 49.42857 8.109285  9.907946
## 49.57143 9.174411 11.032029
## 49.71429 8.644385 10.601264
## 49.85714 9.303761 11.323929
## 50.00000 9.055575 11.159349
## 50.14286 9.482018 11.649796
## 50.28571 9.391955 11.633739
## 50.42857 9.683749 11.988454
## 50.57143 9.681326 12.053621
## 50.71429 9.894527 12.327870
## 50.85714 9.939836 12.436146
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 11.00208 14.23013 22.53299 33.33703 13.67206 10.01205 13.00954 13.11739
##  [9] 12.13500 11.74921 11.74921 11.74921 11.74921 11.74921
## 
## $岩手県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000  8.006648  6.420960
## 49.14286 10.711098  8.848237
## 49.28571 18.923179 17.012260
## 49.42857 29.708886 27.788263
## 49.57143 10.025681  8.095403
## 49.71429  6.347526  4.407642
## 49.85714  9.326954  7.377511
## 50.00000  8.589991  6.193328
## 50.14286  7.276278  4.704228
## 50.28571  6.793877  4.170685
## 50.42857  6.752692  4.107698
## 50.57143  6.711845  4.045227
## 50.71429  6.671325  3.983258
## 50.85714  6.631127  3.921780
## 
## $岩手県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 13.99752 15.58321
## 49.14286 17.74916 19.61202
## 49.28571 26.14280 28.05372
## 49.42857 36.96517 38.88579
## 49.57143 17.31844 19.24872
## 49.71429 13.67658 15.61647
## 49.85714 16.69213 18.64157
## 50.00000 17.64480 20.04146
## 50.14286 16.99371 19.56576
## 50.28571 16.70453 19.32773
## 50.42857 16.74572 19.39071
## 50.57143 16.78657 19.45318
## 50.71429 16.82709 19.51515
## 50.85714 16.86728 19.57663
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 32.59058 37.69811 30.38509 29.60586 23.76580 24.30475 23.23556 25.97458
##  [9] 26.66165 28.21707 27.52706 27.09877 25.75880 25.33525
## 
## $宮城県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 26.96907 23.99322
## 49.14286 31.91001 28.84597
## 49.28571 24.34157 21.14232
## 49.42857 23.55560 20.35279
## 49.57143 17.47134 14.13925
## 49.71429 17.79689 14.35183
## 49.85714 16.22353 12.51158
## 50.00000 18.66030 14.78835
## 50.14286 19.08937 15.08086
## 50.28571 20.56551 16.51503
## 50.42857 19.82938 15.75447
## 50.57143 19.38513 15.30177
## 50.71429 18.01318 13.91289
## 50.85714 17.54443 13.42022
## 
## $宮城県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 38.21208 41.18793
## 49.14286 43.48621 46.55025
## 49.28571 36.42861 39.62785
## 49.42857 35.65611 38.85892
## 49.57143 30.06027 33.39236
## 49.71429 30.81261 34.25767
## 49.85714 30.24759 33.95953
## 50.00000 33.28886 37.16080
## 50.14286 34.23392 38.24243
## 50.28571 35.86862 39.91910
## 50.42857 35.22475 39.29966
## 50.57143 34.81242 38.89578
## 50.71429 33.50442 37.60470
## 50.85714 33.12607 37.25028
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 0.3814625 0.4313360 0.4767464 0.4877680 0.4938386 0.4957559 0.4966255
##  [8] 0.4969346 0.4970637 0.4971121 0.4971316 0.4971391 0.4971420 0.4971432
## 
## $秋田県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                 80%       95%
## 49.00000 -0.9611911 -1.671949
## 49.14286 -0.9257498 -1.644148
## 49.28571 -0.8928288 -1.617838
## 49.42857 -0.8834012 -1.609254
## 49.57143 -0.8782168 -1.604539
## 49.71429 -0.8767466 -1.603306
## 49.85714 -0.8762374 -1.602987
## 50.00000 -0.8762459 -1.603164
## 50.14286 -0.8764212 -1.603500
## 50.28571 -0.8766714 -1.603908
## 50.42857 -0.8769483 -1.604342
## 50.57143 -0.8772363 -1.604787
## 50.71429 -0.8775285 -1.605235
## 50.85714 -0.8778223 -1.605685
## 
## $秋田県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 1.724116 2.434874
## 49.14286 1.788422 2.506820
## 49.28571 1.846322 2.571331
## 49.42857 1.858937 2.584790
## 49.57143 1.865894 2.592216
## 49.71429 1.868258 2.594817
## 49.85714 1.869488 2.596238
## 50.00000 1.870115 2.597033
## 50.14286 1.870549 2.597628
## 50.28571 1.870896 2.598133
## 50.42857 1.871211 2.598605
## 50.57143 1.871514 2.599065
## 50.71429 1.871813 2.599519
## 50.85714 1.872109 2.599971
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 14.51172 16.28775 13.86390 17.69291 15.83019 15.29017 16.53992 16.20252
##  [9] 16.83603 15.91621 17.66734 16.24700 16.51807 16.85489
## 
## $山形県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 12.76082 11.83395
## 49.14286 14.51328 13.57394
## 49.28571 11.97120 10.96926
## 49.42857 15.68894 14.62810
## 49.57143 13.72081 12.60417
## 49.71429 13.08040 11.91062
## 49.85714 14.23413 13.01352
## 50.00000 13.65291 12.30323
## 50.14286 14.18420 12.78040
## 50.28571 13.14049 11.67110
## 50.42857 14.77302 13.24086
## 50.57143 13.23875 11.64628
## 50.71429 13.40006 11.74948
## 50.85714 13.63085 11.92415
## 
## $山形県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 16.26262 17.18950
## 49.14286 18.06221 19.00155
## 49.28571 15.75660 16.75853
## 49.42857 19.69688 20.75772
## 49.57143 17.93957 19.05621
## 49.71429 17.49994 18.66972
## 49.85714 18.84572 20.06633
## 50.00000 18.75213 20.10182
## 50.14286 19.48787 20.89166
## 50.28571 18.69194 20.16132
## 50.42857 20.56167 22.09383
## 50.57143 19.25524 20.84771
## 50.71429 19.63608 21.28665
## 50.85714 20.07893 21.78564
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 12.66967 12.57527 14.67752 13.94963 13.94957 13.39097 13.92825 13.56169
##  [9] 13.62661 14.43306 14.51892 13.99616 13.97896 14.35462
## 
## $福島県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%      95%
## 49.00000  9.784106 8.256582
## 49.14286  9.573448 7.984382
## 49.28571 11.563778 9.915466
## 49.42857 10.727856 9.022354
## 49.57143 10.623272 8.862437
## 49.71429  9.963332 8.148851
## 49.85714 10.402186 8.535599
## 50.00000  9.842073 7.873029
## 50.14286  9.792581 7.762970
## 50.28571 10.487936 8.399514
## 50.42857 10.465741 8.320120
## 50.57143  9.837736 7.636401
## 50.71429  9.717897 7.462224
## 50.85714  9.993325 7.684593
## 
## $福島県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 15.55523 17.08275
## 49.14286 15.57708 17.16615
## 49.28571 17.79125 19.43956
## 49.42857 17.17140 18.87690
## 49.57143 17.27587 19.03670
## 49.71429 16.81861 18.63309
## 49.85714 17.45432 19.32091
## 50.00000 17.28130 19.25034
## 50.14286 17.46063 19.49024
## 50.28571 18.37818 20.46660
## 50.42857 18.57209 20.71771
## 50.57143 18.15458 20.35591
## 50.71429 18.24003 20.49570
## 50.85714 18.71592 21.02465
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 21.08539 19.88057 33.15244 16.74912 15.38198 14.37484 19.83610 13.93168
##  [9] 14.36426 15.00226 19.72458 15.06858 18.80746 14.79079
## 
## $茨城県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%        95%
## 49.00000 13.508624  9.4977243
## 49.14286 11.987613  7.8093328
## 49.28571 24.955482 20.6162723
## 49.42857  8.259030  3.7646493
## 49.57143  6.608555  1.9641859
## 49.71429  5.326952  0.5372885
## 49.85714 10.521826  5.5911467
## 50.00000  4.147751 -1.0315461
## 50.14286  4.280604 -1.0573622
## 50.28571  4.627529 -0.8645236
## 50.42857  9.066710  3.4247772
## 50.57143  4.134913 -1.6530200
## 50.71429  7.604778  1.6744372
## 50.85714  3.325404 -2.7440030
## 
## $茨城県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 28.66216 32.67306
## 49.14286 27.77353 31.95181
## 49.28571 41.34941 45.68862
## 49.42857 25.23920 29.73358
## 49.57143 24.15540 28.79977
## 49.71429 23.42273 28.21240
## 49.85714 29.15037 34.08105
## 50.00000 23.71560 28.89490
## 50.14286 24.44792 29.78589
## 50.28571 25.37700 30.86905
## 50.42857 30.38244 36.02437
## 50.57143 26.00225 31.79018
## 50.71429 30.01014 35.94048
## 50.85714 26.25617 32.32558
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 20.83000 20.65986 19.97850 20.08127 18.38322 18.85174 22.37473 20.04278
##  [9] 21.04705 20.14979 20.75949 19.08325 19.19557 22.45155
## 
## $栃木県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 16.99667 14.96743
## 49.14286 16.74025 14.66533
## 49.28571 15.97446 13.85486
## 49.42857 15.99456 13.83119
## 49.57143 14.21546 12.00919
## 49.71429 14.60450 12.35614
## 49.85714 18.04945 15.75979
## 50.00000 15.46509 13.04181
## 50.14286 16.36912 13.89278
## 50.28571 15.37373 12.84543
## 50.42857 15.88727 13.30806
## 50.57143 14.11673 11.48761
## 50.71429 14.13650 11.45839
## 50.85714 17.30160 14.57539
## 
## $栃木県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 24.66332 26.69257
## 49.14286 24.57947 26.65439
## 49.28571 23.98253 26.10214
## 49.42857 24.16798 26.33136
## 49.57143 22.55097 24.75724
## 49.71429 23.09898 25.34734
## 49.85714 26.70000 28.98967
## 50.00000 24.62046 27.04374
## 50.14286 25.72497 28.20132
## 50.28571 24.92585 27.45415
## 50.42857 25.63172 28.21092
## 50.57143 24.04978 26.67890
## 50.71429 24.25464 26.93274
## 50.85714 27.60150 30.32772
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 33.89644 39.79316 37.91716 34.99079 35.39139 33.94693 32.09042 33.47670
##  [9] 36.55843 36.87122 35.23906 34.34724 34.31058 34.13778
## 
## $群馬県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 28.57090 25.75172
## 49.14286 33.32157 29.89572
## 49.28571 31.07638 27.45508
## 49.42857 28.02450 24.33677
## 49.57143 28.10394 24.24619
## 49.71429 26.53022 22.60405
## 49.85714 24.47472 20.44322
## 50.00000 25.39747 21.12058
## 50.14286 27.86562 23.26392
## 50.28571 27.87400 23.11116
## 50.42857 26.06972 21.21576
## 50.57143 24.99398 20.04267
## 50.71429 24.73109 19.66002
## 50.85714 24.32878 19.13622
## 
## $群馬県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 39.22198 42.04115
## 49.14286 46.26474 49.69059
## 49.28571 44.75795 48.37924
## 49.42857 41.95708 45.64481
## 49.57143 42.67885 46.53660
## 49.71429 41.36365 45.28982
## 49.85714 39.70612 43.73763
## 50.00000 41.55593 45.83282
## 50.14286 45.25125 49.85295
## 50.28571 45.86843 50.63127
## 50.42857 44.40840 49.26236
## 50.57143 43.70050 48.65181
## 50.71429 43.89007 48.96115
## 50.85714 43.94677 49.13934
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 158.3367 171.4859 174.6140 183.1537 154.8662 141.8478 168.5531 159.8950
##  [9] 168.8374 167.3561 171.3469 160.3127 158.1569 164.7300
## 
## $埼玉県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 140.2891 130.7353
## 49.14286 152.9007 143.0624
## 49.28571 155.5064 145.3914
## 49.42857 163.5375 153.1533
## 49.57143 134.7543 124.1077
## 49.71429 121.2521 110.3494
## 49.85714 147.4847 136.3318
## 50.00000 135.4159 122.4574
## 50.14286 143.5326 130.1371
## 50.28571 141.2517 127.4329
## 50.42857 144.4667 130.2373
## 50.57143 132.6785 118.0499
## 50.71429 129.7887 114.7715
## 50.85714 135.6463 120.2504
## 
## $埼玉県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 176.3842 185.9380
## 49.14286 190.0710 199.9094
## 49.28571 193.7216 203.8366
## 49.42857 202.7699 213.1541
## 49.57143 174.9781 185.6247
## 49.71429 162.4435 173.3462
## 49.85714 189.6214 200.7743
## 50.00000 184.3741 197.3325
## 50.14286 194.1422 207.5377
## 50.28571 193.4604 207.2792
## 50.42857 198.2270 212.4565
## 50.57143 187.9468 202.5755
## 50.71429 186.5250 201.5422
## 50.85714 193.8136 209.2096
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 112.8025 124.2765 112.5497 113.0951 113.7012 104.2643 116.7096 116.5019
##  [9] 126.2514 119.9702 121.0079 119.7830 115.6257 121.2426
## 
## $千葉県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000  99.23621  92.05466
## 49.14286 108.93622 100.81554
## 49.28571  96.46161  87.94506
## 49.42857  96.51408  87.73664
## 49.57143  96.71007  87.71550
## 49.71429  86.89557  77.70111
## 49.85714  98.97926  89.59338
## 50.00000  97.49326  87.43069
## 50.14286 106.52102  96.07639
## 50.28571  99.68256  88.94295
## 50.42857 100.22322  89.22051
## 50.57143  98.52846  87.27702
## 50.71429  93.91691  82.42499
## 50.85714  99.09084  87.36439
## 
## $千葉県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 126.3688 133.5503
## 49.14286 139.6169 147.7376
## 49.28571 128.6379 137.1544
## 49.42857 129.6760 138.4535
## 49.57143 130.6924 139.6870
## 49.71429 121.6330 130.8275
## 49.85714 134.4399 143.8258
## 50.00000 135.5105 145.5731
## 50.14286 145.9817 156.4264
## 50.28571 140.2578 150.9974
## 50.42857 141.7925 152.7952
## 50.57143 141.0374 152.2889
## 50.71429 137.3344 148.8263
## 50.85714 143.3944 155.1209
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 594.6420 630.9779 611.6318 652.3820 486.0950 379.3249 462.8235 612.4389
##  [9] 652.9759 634.5116 675.1278 508.4032 401.1116 484.0726
## 
## $東京都$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 541.6197 513.5514
## 49.14286 570.5368 538.5412
## 49.28571 547.5567 513.6374
## 49.42857 585.6708 550.3561
## 49.57143 417.1447 380.6447
## 49.71429 308.3619 270.7964
## 49.85714 390.0142 351.4714
## 50.00000 531.0064 487.8987
## 50.14286 567.2683 521.8974
## 50.28571 545.6856 498.6639
## 50.42857 583.6426 535.2133
## 50.57143 414.5201 364.8214
## 50.71429 305.0229 254.1567
## 50.85714 385.9394 333.9908
## 
## $東京都$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 647.6643 675.7326
## 49.14286 691.4190 723.4145
## 49.28571 675.7069 709.6263
## 49.42857 719.0932 754.4079
## 49.57143 555.0452 591.5452
## 49.71429 450.2878 487.8533
## 49.85714 535.6328 574.1757
## 50.00000 693.8714 736.9792
## 50.14286 738.6835 784.0544
## 50.28571 723.3376 770.3592
## 50.42857 766.6130 815.0424
## 50.57143 602.2863 651.9850
## 50.71429 497.2003 548.0666
## 50.85714 582.2058 634.1544
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 260.6472 269.7294 278.8439 261.2886 237.6378 165.4313 235.0791 283.6318
##  [9] 290.7191 299.4042 284.4074 257.0413 188.1399 253.8335
## 
## $神奈川県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 237.7888 225.6883
## 49.14286 244.2146 230.7079
## 49.28571 251.6359 237.2329
## 49.42857 233.8821 219.3740
## 49.57143 209.7984 195.0611
## 49.71429 137.0405 122.0113
## 49.85714 205.7808 190.2712
## 50.00000 250.7955 233.4131
## 50.14286 256.1460 237.8441
## 50.28571 263.4827 244.4670
## 50.42857 247.6874 228.2490
## 50.57143 219.4242 199.5109
## 50.71429 149.5737 129.1580
## 50.85714 214.2082 193.2318
## 
## $神奈川県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 283.5055 295.6060
## 49.14286 295.2441 308.7508
## 49.28571 306.0518 320.4548
## 49.42857 288.6951 303.2032
## 49.57143 265.4772 280.2145
## 49.71429 193.8221 208.8513
## 49.85714 264.3774 279.8870
## 50.00000 316.4681 333.8506
## 50.14286 325.2923 343.5942
## 50.28571 335.3257 354.3414
## 50.42857 321.1275 340.5659
## 50.57143 294.6584 314.5718
## 50.71429 226.7060 247.1217
## 50.85714 293.4588 314.4352
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 5.956766 6.622087 6.093151 6.136638 6.235138 6.145706 6.226904 6.153182
##  [9] 6.220117 6.159344 6.214522 6.164424 6.209910 6.168612
## 
## $新潟県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 2.933927 1.3337326
## 49.14286 3.448041 1.7678026
## 49.28571 2.911919 1.2278762
## 49.42857 2.886438 1.1658862
## 49.57143 2.916419 1.1595954
## 49.71429 2.811258 1.0461071
## 49.85714 2.831797 1.0345360
## 50.00000 2.739820 0.9328957
## 50.14286 2.752229 0.9164398
## 50.28571 2.671080 0.8245051
## 50.42857 2.676579 0.8037050
## 50.57143 2.604364 0.7197827
## 50.71429 2.604049 0.6952214
## 50.85714 2.539236 0.6179599
## 
## $新潟県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 8.979606 10.57980
## 49.14286 9.796133 11.47637
## 49.28571 9.274383 10.95843
## 49.42857 9.386838 11.10739
## 49.57143 9.553856 11.31068
## 49.71429 9.480155 11.24531
## 49.85714 9.622011 11.41927
## 50.00000 9.566543 11.37347
## 50.14286 9.688005 11.52379
## 50.28571 9.647608 11.49418
## 50.42857 9.752465 11.62534
## 50.57143 9.724484 11.60907
## 50.71429 9.815771 11.72460
## 50.85714 9.797988 11.71926
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 1.832224 1.808485 1.786001 1.764707 1.744539 1.725438 1.707348 1.690214
##  [9] 1.673987 1.658618 1.644062 1.630276 1.617219 1.604853
## 
## $富山県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                 80%       95%
## 49.00000 -0.5922498 -1.875689
## 49.14286 -0.7715899 -2.137399
## 49.28571 -0.9260632 -2.361743
## 49.42857 -1.0605114 -2.556092
## 49.57143 -1.1784544 -2.725794
## 49.71429 -1.2825574 -2.874894
## 49.85714 -1.3749015 -3.006546
## 50.00000 -1.4571507 -3.123265
## 50.14286 -1.5306616 -3.227100
## 50.28571 -1.5965567 -3.319742
## 50.42857 -1.6557767 -3.402606
## 50.57143 -1.7091183 -3.476887
## 50.71429 -1.7572622 -3.543605
## 50.85714 -1.8007941 -3.603635
## 
## $富山県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 4.256698 5.540137
## 49.14286 4.388559 5.754369
## 49.28571 4.498066 5.933746
## 49.42857 4.589926 6.085506
## 49.57143 4.667533 6.214873
## 49.71429 4.733434 6.325771
## 49.85714 4.789597 6.421241
## 50.00000 4.837579 6.503693
## 50.14286 4.878635 6.575074
## 50.28571 4.913792 6.636978
## 50.42857 4.943900 6.690729
## 50.57143 4.969670 6.737438
## 50.71429 4.991700 6.778043
## 50.85714 5.010500 6.813340
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 5.828609 5.828609 5.828609 5.828609 5.828609 5.828609 5.828609 5.828609
##  [9] 5.828609 5.828609 5.828609 5.828609 5.828609 5.828609
## 
## $石川県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%         95%
## 49.00000 2.2259033  0.31874593
## 49.14286 2.0262072  0.01333712
## 49.28571 1.8364880 -0.27681343
## 49.42857 1.6553847 -0.55378702
## 49.57143 1.4818203 -0.81923087
## 49.71429 1.3149250 -1.07447517
## 49.85714 1.1539845 -1.32061243
## 50.00000 0.9984035 -1.55855304
## 50.14286 0.8476798 -1.78906512
## 50.28571 0.7013849 -2.01280383
## 50.42857 0.5591500 -2.23033331
## 50.57143 0.4206548 -2.44214352
## 50.71429 0.2856188 -2.64866316
## 50.85714 0.1537953 -2.85026992
## 
## $石川県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%      95%
## 49.00000  9.431316 11.33847
## 49.14286  9.631012 11.64388
## 49.28571  9.820731 11.93403
## 49.42857 10.001834 12.21101
## 49.57143 10.175399 12.47645
## 49.71429 10.342294 12.73169
## 49.85714 10.503234 12.97783
## 50.00000 10.658815 13.21577
## 50.14286 10.809539 13.44628
## 50.28571 10.955834 13.67002
## 50.42857 11.098069 13.88755
## 50.57143 11.236564 14.09936
## 50.71429 11.371600 14.30588
## 50.85714 11.503424 14.50749
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 0.6800766 0.7949651 0.8208614 0.8429707 0.8618468 0.8779626 0.8917216
##  [8] 0.9034686 0.9134977 0.9220603 0.9293706 0.9356120 0.9409406 0.9454900
## 
## $福井県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 -1.322903 -2.383217
## 49.14286 -1.352860 -2.489849
## 49.28571 -1.524160 -2.765539
## 49.42857 -1.635924 -2.948172
## 49.57143 -1.710244 -3.071826
## 49.71429 -1.759986 -3.156431
## 49.85714 -1.793214 -3.214532
## 50.00000 -1.815204 -3.254383
## 50.14286 -1.829505 -3.281564
## 50.28571 -1.838542 -3.299917
## 50.42857 -1.843990 -3.312119
## 50.57143 -1.847012 -3.320044
## 50.71429 -1.848415 -3.325011
## 50.85714 -1.848763 -3.327951
## 
## $福井県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 2.683057 3.743370
## 49.14286 2.942790 4.079779
## 49.28571 3.165882 4.407262
## 49.42857 3.321866 4.634114
## 49.57143 3.433938 4.795520
## 49.71429 3.515911 4.912356
## 49.85714 3.576657 4.997976
## 50.00000 3.622141 5.061320
## 50.14286 3.656501 5.108559
## 50.28571 3.682663 5.144038
## 50.42857 3.702731 5.170860
## 50.57143 3.718236 5.191268
## 50.71429 3.730297 5.206892
## 50.85714 3.739743 5.218931
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 5.924501 8.186390 6.512746 8.158092 7.035593 6.468877 5.572877 6.188258
##  [9] 5.890226 6.167786 6.232731 5.111986 5.401248 5.567332
## 
## $山梨県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 3.635833 2.4242861
## 49.14286 5.672233 4.3413196
## 49.28571 3.777573 2.3296593
## 49.42857 5.386686 3.9195926
## 49.57143 4.228422 2.7423953
## 49.71429 3.626392 2.1216704
## 49.85714 2.695510 1.1723237
## 50.00000 3.307731 1.7828722
## 50.14286 2.993607 1.4602292
## 50.28571 3.255680 1.7141038
## 50.42857 3.295177 1.7401293
## 50.57143 2.149203 0.5808001
## 50.71429 2.413449 0.8318043
## 50.85714 2.554725 0.9599473
## 
## $山梨県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000  8.213168  9.424715
## 49.14286 10.700546 12.031460
## 49.28571  9.247919 10.695832
## 49.42857 10.929498 12.396592
## 49.57143  9.842763 11.328790
## 49.71429  9.311363 10.816084
## 49.85714  8.450245  9.973431
## 50.00000  9.068786 10.593645
## 50.14286  8.786845 10.320223
## 50.28571  9.079893 10.621469
## 50.42857  9.170285 10.725332
## 50.57143  8.074769  9.643171
## 50.71429  8.389047  9.970693
## 50.85714  8.579940 10.174717
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 18.34456 18.34456 18.34456 18.34456 18.34456 18.34456 18.34456 18.34456
##  [9] 18.34456 18.34456 18.34456 18.34456 18.34456 18.34456
## 
## $長野県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 14.60989 12.632880
## 49.14286 14.35090 12.236778
## 49.28571 14.10770 11.864844
## 49.42857 13.87773 11.513130
## 49.57143 13.65903 11.178658
## 49.71429 13.45009 10.859116
## 49.85714 13.24972 10.552668
## 50.00000 13.05693 10.257824
## 50.14286 12.87093  9.973359
## 50.28571 12.69104  9.698247
## 50.42857 12.51670  9.431623
## 50.57143 12.34743  9.172746
## 50.71429 12.18281  8.920979
## 50.85714 12.02248  8.675765
## 
## $長野県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 22.07923 24.05624
## 49.14286 22.33823 24.45235
## 49.28571 22.58142 24.82428
## 49.42857 22.81139 25.17599
## 49.57143 23.03009 25.51047
## 49.71429 23.23903 25.83001
## 49.85714 23.43941 26.13646
## 50.00000 23.63220 26.43130
## 50.14286 23.81820 26.71577
## 50.28571 23.99808 26.99088
## 50.42857 24.17242 27.25750
## 50.57143 24.34169 27.51638
## 50.71429 24.50631 27.76814
## 50.85714 24.66665 28.01336
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 34.78031 35.06315 29.25819 40.59313 36.11395 26.06290 31.99336 32.34158
##  [9] 32.34158 32.34158 32.34158 32.34158 32.34158 32.34158
## 
## $岐阜県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 29.68210 26.98327
## 49.14286 29.77288 26.97238
## 49.28571 23.78259 20.88399
## 49.42857 34.93828 31.94478
## 49.57143 30.28535 27.19988
## 49.71429 20.06559 16.89080
## 49.85714 25.83196 22.57031
## 50.00000 25.30231 21.57594
## 50.14286 25.03337 21.16464
## 50.28571 24.77399 20.76794
## 50.42857 24.52320 20.38440
## 50.57143 24.28022 20.01279
## 50.71429 24.04434 19.65205
## 50.85714 23.81499 19.30129
## 
## $岐阜県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 39.87853 42.57736
## 49.14286 40.35343 43.15393
## 49.28571 34.73379 37.63240
## 49.42857 46.24799 49.24148
## 49.57143 41.94254 45.02802
## 49.71429 32.06020 35.23499
## 49.85714 38.15477 41.41642
## 50.00000 39.38085 43.10721
## 50.14286 39.64978 43.51851
## 50.28571 39.90917 43.91521
## 50.42857 40.15995 44.29875
## 50.57143 40.40294 44.67036
## 50.71429 40.63881 45.03110
## 50.85714 40.86816 45.38186
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 35.23994 37.79077 28.63502 33.21297 30.86559 26.50449 23.22007 32.14866
##  [9] 34.23378 27.06560 30.64975 28.81195 25.39757 22.82614
## 
## $静岡県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 27.801418 23.863702
## 49.14286 29.202366 24.655942
## 49.28571 19.486461 14.643509
## 49.42857 23.536628 18.414284
## 49.57143 20.688806 15.301541
## 49.71429 15.850734 10.210979
## 49.85714 12.109807  6.228391
## 50.00000 19.946340 13.486819
## 50.14286 21.314192 14.474973
## 50.28571 13.554352  6.401931
## 50.42857 16.571696  9.119225
## 50.57143 14.189043  6.448144
## 50.71429 10.249391  2.230430
## 50.85714  7.170309 -1.117389
## 
## $静岡県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 42.67846 46.61618
## 49.14286 46.37916 50.92559
## 49.28571 37.78357 42.62652
## 49.42857 42.88930 48.01165
## 49.57143 41.04238 46.42964
## 49.71429 37.15824 42.79799
## 49.85714 34.33033 40.21174
## 50.00000 44.35099 50.81051
## 50.14286 47.15338 53.99260
## 50.28571 40.57684 47.72926
## 50.42857 44.72780 52.18027
## 50.57143 43.43486 51.17576
## 50.71429 40.54575 48.56471
## 50.85714 38.48198 46.76968
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 240.0351 252.9539 216.7104 225.5619 187.0631 134.0715 218.0010 238.2273
##  [9] 245.1998 229.6590 232.4652 191.5138 138.2764 214.8810
## 
## $愛知県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 221.37233 211.49285
## 49.14286 231.40565 219.99866
## 49.28571 192.42025 179.56182
## 49.42857 199.44211 185.61514
## 49.57143 159.08124 144.26856
## 49.71429 104.80923  89.31872
## 49.85714 187.38988 171.18531
## 50.00000 200.98151 181.26479
## 50.14286 204.70645 183.27058
## 50.28571 186.48948 163.63690
## 50.42857 187.02086 162.96405
## 50.57143 144.14386 119.06772
## 50.71429  89.23694  63.27701
## 50.85714 164.40797 137.68917
## 
## $愛知県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 258.6979 268.5773
## 49.14286 274.5022 285.9092
## 49.28571 241.0005 253.8590
## 49.42857 251.6816 265.5086
## 49.57143 215.0449 229.8576
## 49.71429 163.3338 178.8243
## 49.85714 248.6122 264.8168
## 50.00000 275.4731 295.1898
## 50.14286 285.6931 307.1290
## 50.28571 272.8286 295.6812
## 50.42857 277.9096 301.9664
## 50.57143 238.8838 263.9599
## 50.71429 187.3159 213.2758
## 50.85714 265.3540 292.0728
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 16.35114 16.96067 15.70631 15.55605 14.69786 17.84529 15.16045 14.81528
##  [9] 15.74847 15.40941 15.08570 15.82146 15.39126 15.19713
## 
## $三重県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%      95%
## 49.00000 12.169847 9.956406
## 49.14286 12.306447 9.842653
## 49.28571 10.643589 7.963550
## 49.42857 10.317610 7.544546
## 49.57143  9.414196 6.617191
## 49.71429 12.417160 9.543681
## 49.85714  9.660911 6.749633
## 50.00000  8.933440 5.819781
## 50.14286  9.576679 6.309529
## 50.28571  9.057846 5.695532
## 50.42857  8.641033 5.229434
## 50.57143  9.239507 5.755234
## 50.71429  8.704172 5.164241
## 50.85714  8.438599 4.860850
## 
## $三重県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 20.53243 22.74587
## 49.14286 21.61488 24.07868
## 49.28571 20.76902 23.44906
## 49.42857 20.79450 23.56756
## 49.57143 19.98153 22.77853
## 49.71429 23.27342 26.14690
## 49.85714 20.65998 23.57126
## 50.00000 20.69712 23.81078
## 50.14286 21.92027 25.18742
## 50.28571 21.76097 25.12328
## 50.42857 21.53036 24.94196
## 50.57143 22.40341 25.88768
## 50.71429 22.07835 25.61828
## 50.85714 21.95566 25.53340
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 6.406446 6.996653 6.498963 6.672027 6.516137 6.566482 6.517497 6.531996
##  [9] 6.516550 6.520672 6.515783 6.516935 6.515381 6.515695
## 
## $滋賀県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%        95%
## 49.00000 2.507354  0.4432995
## 49.14286 3.034165  0.9365499
## 49.28571 2.124565 -0.1911018
## 49.42857 2.214965 -0.1444606
## 49.57143 1.903069 -0.5389416
## 49.71429 1.870935 -0.6147376
## 49.85714 1.722148 -0.8163570
## 50.00000 1.657537 -0.9228458
## 50.14286 1.559265 -1.0649634
## 50.28571 1.487408 -1.1770415
## 50.42857 1.406329 -1.2984532
## 50.57143 1.334075 -1.4095648
## 50.71429 1.259784 -1.5223613
## 50.85714 1.188867 -1.6309861
## 
## $滋賀県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 10.30554 12.36959
## 49.14286 10.95914 13.05676
## 49.28571 10.87336 13.18903
## 49.42857 11.12909 13.48852
## 49.57143 11.12920 13.57121
## 49.71429 11.26203 13.74770
## 49.85714 11.31285 13.85135
## 50.00000 11.40645 13.98684
## 50.14286 11.47384 14.09806
## 50.28571 11.55394 14.21838
## 50.42857 11.62524 14.33002
## 50.57143 11.69979 14.44343
## 50.71429 11.77098 14.55312
## 50.85714 11.84252 14.66238
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 69.95417 61.13752 64.10302 68.35078 65.76856 60.94719 69.88198 70.69018
##  [9] 66.89468 67.25530 69.39100 67.46573 65.51434 68.40932
## 
## $京都府$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 61.92408 57.67321
## 49.14286 51.98065 47.13329
## 49.28571 54.69802 49.71932
## 49.42857 58.53042 53.33184
## 49.57143 55.54971 50.14018
## 49.71429 50.34482 44.73226
## 49.85714 58.90948 53.10099
## 50.00000 58.88189 52.63096
## 50.14286 54.52966 47.98401
## 50.28571 54.47365 47.70746
## 50.42857 56.17332 49.17631
## 50.57143 53.82596 46.60550
## 50.71429 51.46514 44.02795
## 50.85714 53.96230 46.31451
## 
## $京都府$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 77.98426 82.23513
## 49.14286 70.29439 75.14175
## 49.28571 73.50801 78.48671
## 49.42857 78.17113 83.36971
## 49.57143 75.98740 81.39693
## 49.71429 71.54956 77.16211
## 49.85714 80.85448 86.66297
## 50.00000 82.49847 88.74940
## 50.14286 79.25970 85.80535
## 50.28571 80.03695 86.80315
## 50.42857 82.60868 89.60569
## 50.57143 81.10550 88.32596
## 50.71429 79.56353 87.00072
## 50.85714 82.85633 90.50412
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 405.9100 381.4971 358.0884 400.0247 321.9498 235.7008 303.9921 389.4678
##  [9] 373.6456 354.2383 389.0061 324.2772 252.7715 309.3892
## 
## $大阪府$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 370.5651 351.8547
## 49.14286 340.9052 319.4172
## 49.28571 315.9610 293.6602
## 49.42857 356.4158 333.3307
## 49.57143 276.9082 253.0646
## 49.71429 189.2707 164.6921
## 49.85714 256.2138 230.9214
## 50.00000 333.6839 304.1537
## 50.14286 313.9810 282.3965
## 50.28571 292.2380 259.4170
## 50.42857 324.7549 290.7424
## 50.57143 257.8513 222.6875
## 50.71429 184.2399 147.9614
## 50.85714 238.8147 201.4548
## 
## $大阪府$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 441.2548 459.9653
## 49.14286 422.0889 443.5769
## 49.28571 400.2157 422.5166
## 49.42857 443.6335 466.7186
## 49.57143 366.9913 390.8349
## 49.71429 282.1310 306.7096
## 49.85714 351.7705 377.0629
## 50.00000 445.2517 474.7819
## 50.14286 433.3101 464.8947
## 50.28571 416.2386 449.0596
## 50.42857 453.2573 487.2699
## 50.57143 390.7031 425.8668
## 50.71429 321.3031 357.5815
## 50.85714 379.9637 417.3236
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 134.24182 143.11016 107.00647 132.12734 113.84948  83.67417 132.12073
##  [8] 129.98906 137.26153 107.65473 128.25508 113.26633  88.52110 128.24966
## 
## $兵庫県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 120.24890 112.84149
## 49.14286 128.25938 120.39786
## 49.28571  91.34475  83.05394
## 49.42857 115.69466 106.99572
## 49.57143  96.68042  87.59167
## 49.71429  65.79904  56.33652
## 49.85714 113.56638 103.74430
## 50.00000 108.05051  96.43695
## 50.14286 114.11896 101.86802
## 50.28571  83.36775  70.51100
## 50.42857 102.87524  89.43996
## 50.57143  86.83879  72.84889
## 50.71429  61.08583  46.56248
## 50.85714  99.84240  84.80450
## 
## $兵庫県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 148.2347 155.6422
## 49.14286 157.9609 165.8225
## 49.28571 122.6682 130.9590
## 49.42857 148.5600 157.2589
## 49.57143 131.0185 140.1073
## 49.71429 101.5493 111.0118
## 49.85714 150.6751 160.4972
## 50.00000 151.9276 163.5412
## 50.14286 160.4041 172.6550
## 50.28571 131.9417 144.7985
## 50.42857 153.6349 167.0702
## 50.57143 139.6939 153.6838
## 50.71429 115.9564 130.4797
## 50.85714 156.6569 171.6948
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 19.62455 23.46448 24.69882 23.86527 22.28553 21.13829 21.30044 22.04259
##  [9] 22.71239 22.83969 22.51773 22.15005 21.99748 22.10190
## 
## $奈良県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 15.04329 12.61812
## 49.14286 18.56207 15.96688
## 49.28571 19.41366 16.61586
## 49.42857 18.54787 15.73301
## 49.57143 16.94681 14.12067
## 49.71429 15.66357 12.76542
## 49.85714 15.62475 12.62023
## 50.00000 16.07965 12.92305
## 50.14286 16.54756 13.28410
## 50.28571 16.53805 13.20216
## 50.42857 16.10807 12.71500
## 50.57143 15.62948 12.17769
## 50.71429 15.33852 11.81347
## 50.85714 15.28928 11.68290
## 
## $奈良県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 24.20581 26.63098
## 49.14286 28.36690 30.96208
## 49.28571 29.98399 32.78178
## 49.42857 29.18267 31.99753
## 49.57143 27.62425 30.45040
## 49.71429 26.61302 29.51117
## 49.85714 26.97613 29.98066
## 50.00000 28.00553 31.16212
## 50.14286 28.87721 32.14067
## 50.28571 29.14133 32.47722
## 50.42857 28.92740 32.32047
## 50.57143 28.67063 32.12241
## 50.71429 28.65645 32.18149
## 50.85714 28.91451 32.52089
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 5.160658 5.368483 5.536927 6.150895 4.959088 6.478448 5.473277 4.995608
##  [9] 5.315666 5.619653 5.981553 6.383578 6.590011 6.080693
## 
## $和歌山県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 2.912581 1.7225209
## 49.14286 2.952998 1.6743182
## 49.28571 3.027356 1.6988699
## 49.42857 3.550641 2.1741494
## 49.57143 2.271207 0.8483296
## 49.71429 3.705709 2.2379100
## 49.85714 2.618201 1.1068155
## 50.00000 2.089644 0.5513189
## 50.14286 2.343044 0.7694322
## 50.28571 2.579221 0.9697127
## 50.42857 2.874790 1.2301685
## 50.57143 3.211871 1.5328709
## 50.71429 3.354664 1.6419747
## 50.85714 2.782933 1.0372050
## 
## $和歌山県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 7.408735  8.598795
## 49.14286 7.783967  9.062647
## 49.28571 8.046497  9.374983
## 49.42857 8.751149 10.127641
## 49.57143 7.646968  9.069846
## 49.71429 9.251186 10.718985
## 49.85714 8.328353  9.839738
## 50.00000 7.901573  9.439898
## 50.14286 8.288289  9.861901
## 50.28571 8.660086 10.269594
## 50.42857 9.088316 10.732937
## 50.57143 9.555285 11.234285
## 50.71429 9.825358 11.538047
## 50.85714 9.378452 11.124180
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 0.3711657 0.3857176 0.3965097 0.4045134 0.4104492 0.4148513 0.4181161
##  [8] 0.4205373 0.4223330 0.4236647 0.4246524 0.4253848 0.4259281 0.4263309
## 
## $鳥取県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                 80%       95%
## 49.00000 -0.5717362 -1.070878
## 49.14286 -0.5726010 -1.079904
## 49.28571 -0.5710328 -1.083219
## 49.42857 -0.5687268 -1.083929
## 49.57143 -0.5664350 -1.083566
## 49.71429 -0.5644547 -1.082868
## 49.85714 -0.5628683 -1.082170
## 50.00000 -0.5616626 -1.081608
## 50.14286 -0.5607878 -1.081220
## 50.28571 -0.5601848 -1.081003
## 50.42857 -0.5597981 -1.080935
## 50.57143 -0.5595796 -1.080988
## 50.71429 -0.5594904 -1.081139
## 50.85714 -0.5594993 -1.081366
## 
## $鳥取県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 1.314068 1.813210
## 49.14286 1.344036 1.851339
## 49.28571 1.364052 1.876238
## 49.42857 1.377754 1.892956
## 49.57143 1.387333 1.904465
## 49.71429 1.394157 1.912571
## 49.85714 1.399100 1.918402
## 50.00000 1.402737 1.922683
## 50.14286 1.405454 1.925886
## 50.28571 1.407514 1.928333
## 50.42857 1.409103 1.930239
## 50.57143 1.410349 1.931758
## 50.71429 1.411346 1.932995
## 50.85714 1.412161 1.934028
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## 
## $島根県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 -5.989485 -9.424812
## 49.14286 -5.989485 -9.424812
## 49.28571 -5.989485 -9.424812
## 49.42857 -5.989485 -9.424812
## 49.57143 -5.989485 -9.424812
## 49.71429 -5.989485 -9.424812
## 49.85714 -5.989485 -9.424812
## 50.00000 -5.989485 -9.424812
## 50.14286 -5.989485 -9.424812
## 50.28571 -5.989485 -9.424812
## 50.42857 -5.989485 -9.424812
## 50.57143 -5.989485 -9.424812
## 50.71429 -5.989485 -9.424812
## 50.85714 -5.989485 -9.424812
## 
## $島根県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 6.989485 10.42481
## 49.14286 6.989485 10.42481
## 49.28571 6.989485 10.42481
## 49.42857 6.989485 10.42481
## 49.57143 6.989485 10.42481
## 49.71429 6.989485 10.42481
## 49.85714 6.989485 10.42481
## 50.00000 6.989485 10.42481
## 50.14286 6.989485 10.42481
## 50.28571 6.989485 10.42481
## 50.42857 6.989485 10.42481
## 50.57143 6.989485 10.42481
## 50.71429 6.989485 10.42481
## 50.85714 6.989485 10.42481
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 19.47317 19.12026 19.27837 19.74919 21.29758 20.29688 19.73787 19.64930
##  [9] 19.64833 19.68261 19.73108 19.78526 19.84174 19.89915
## 
## $岡山県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 15.78063 13.82592
## 49.14286 14.95498 12.75001
## 49.28571 14.93895 12.64180
## 49.42857 15.31102 12.96159
## 49.57143 16.78502 14.39622
## 49.71429 15.71917 13.29588
## 49.85714 15.09896 12.64328
## 50.00000 14.88159 12.35771
## 50.14286 14.79134 12.22021
## 50.28571 14.75060 12.13975
## 50.42857 14.72991 12.08244
## 50.57143 14.71771 12.03511
## 50.71429 14.70940 11.99250
## 50.85714 14.70310 11.95248
## 
## $岡山県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 23.16571 25.12042
## 49.14286 23.28555 25.49052
## 49.28571 23.61779 25.91494
## 49.42857 24.18736 26.53679
## 49.57143 25.81013 28.19893
## 49.71429 24.87460 27.29789
## 49.85714 24.37678 26.83247
## 50.00000 24.41702 26.94089
## 50.14286 24.50532 27.07645
## 50.28571 24.61463 27.22548
## 50.42857 24.73226 27.37972
## 50.57143 24.85282 27.53542
## 50.71429 24.97409 27.69099
## 50.85714 25.09519 27.84581
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1]  89.10888 111.77066  79.20360 109.78682  95.92681  85.66706 112.75050
##  [8]  88.84250 104.52499 115.27955  94.45134 112.58792 103.73906  95.54686
## 
## $広島県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000  83.17699  80.03684
## 49.14286 104.93869 101.32206
## 49.28571  71.67114  67.68369
## 49.42857 100.52615  95.62385
## 49.57143  86.31073  81.22028
## 49.71429  74.66682  68.84365
## 49.85714 100.77529  94.43600
## 50.00000  76.21485  69.53018
## 50.14286  90.39426  82.91391
## 50.28571 100.58531  92.80665
## 50.42857  78.87391  70.62771
## 50.57143  95.96755  87.16927
## 50.71429  86.64599  77.59746
## 50.85714  77.42319  67.82910
## 
## $広島県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000  95.04076  98.18091
## 49.14286 118.60264 122.21927
## 49.28571  86.73605  90.72350
## 49.42857 119.04748 123.94978
## 49.57143 105.54290 110.63334
## 49.71429  96.66730 102.49048
## 49.85714 124.72571 131.06501
## 50.00000 101.47014 108.15481
## 50.14286 118.65571 126.13606
## 50.28571 129.97380 137.75246
## 50.42857 110.02878 118.27497
## 50.57143 129.20828 138.00657
## 50.71429 120.83214 129.88067
## 50.85714 113.67052 123.26461
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 3.028158 3.120570 2.677834 2.840547 2.618858 2.743953 2.976190 2.861955
##  [9] 3.140941 2.841363 3.076606 2.911284 2.891959 2.939678
## 
## $山口県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                 80%        95%
## 49.00000  0.4318547 -0.9425454
## 49.14286  0.3196259 -1.1631044
## 49.28571 -0.3181565 -1.9041381
## 49.42857 -0.2442729 -1.8772781
## 49.57143 -0.5637533 -2.2485261
## 49.71429 -0.4815287 -2.1889954
## 49.85714 -0.3030466 -2.0389694
## 50.00000 -0.5407692 -2.3420624
## 50.14286 -0.3275601 -2.1636737
## 50.28571 -0.6638622 -2.5194166
## 50.42857 -0.4666999 -2.3424127
## 50.57143 -0.6532612 -2.5402172
## 50.71429 -0.6964253 -2.5960012
## 50.85714 -0.6620909 -2.5687523
## 
## $山口県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 5.624462 6.998862
## 49.14286 5.921515 7.404245
## 49.28571 5.673824 7.259806
## 49.42857 5.925368 7.558373
## 49.57143 5.801470 7.486243
## 49.71429 5.969434 7.676901
## 49.85714 6.255426 7.991349
## 50.00000 6.264678 8.065972
## 50.14286 6.609442 8.445556
## 50.28571 6.346589 8.202144
## 50.42857 6.619912 8.495625
## 50.57143 6.475829 8.362785
## 50.71429 6.480343 8.379919
## 50.85714 6.541447 8.448109
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1]  0.03674700  0.01347236 -0.18740233 -0.05946375  0.13174028  0.16114062
##  [7] -0.16959666  0.19229723  0.21272673  0.24610369  0.23825225  0.20984901
## [13]  0.20319728  0.24901146
## 
## $徳島県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000 -1.634383 -2.519026
## 49.14286 -1.740970 -2.669716
## 49.28571 -1.959674 -2.897858
## 49.42857 -1.849387 -2.796915
## 49.57143 -1.675662 -2.632443
## 49.71429 -1.663574 -2.629519
## 49.85714 -2.011460 -2.986483
## 50.00000 -1.737357 -2.758854
## 50.14286 -1.749227 -2.787823
## 50.28571 -1.738574 -2.789200
## 50.42857 -1.768892 -2.831411
## 50.57143 -1.819514 -2.893793
## 50.71429 -1.848143 -2.934056
## 50.85714 -1.824073 -2.921497
## 
## $徳島県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 1.707877 2.592520
## 49.14286 1.767915 2.696661
## 49.28571 1.584870 2.523054
## 49.42857 1.730460 2.677988
## 49.57143 1.939143 2.895924
## 49.71429 1.985855 2.951800
## 49.85714 1.672266 2.647290
## 50.00000 2.121951 3.143448
## 50.14286 2.174681 3.213277
## 50.28571 2.230782 3.281407
## 50.42857 2.245397 3.307915
## 50.57143 2.239212 3.313491
## 50.71429 2.254537 3.340451
## 50.85714 2.322096 3.419520
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 2.108751 2.437859 2.535547 2.564544 2.573151 2.575705 2.576464 2.576689
##  [9] 2.576756 2.576776 2.576781 2.576783 2.576784 2.576784
## 
## $香川県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%        95%
## 49.00000 0.2433619 -0.7441153
## 49.14286 0.4526200 -0.5983018
## 49.28571 0.5235117 -0.5415955
## 49.42857 0.5402120 -0.5314044
## 49.57143 0.5397564 -0.5366575
## 49.71429 0.5341326 -0.5466108
## 49.85714 0.5269866 -0.5579411
## 50.00000 0.5194085 -0.5696499
## 50.14286 0.5117225 -0.5814401
## 50.28571 0.5040246 -0.5932235
## 50.42857 0.4963432 -0.6049743
## 50.57143 0.4886865 -0.6166850
## 50.71429 0.4810568 -0.6283540
## 50.85714 0.4734544 -0.6399809
## 
## $香川県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 3.974139 4.961617
## 49.14286 4.423097 5.474019
## 49.28571 4.547582 5.612690
## 49.42857 4.588875 5.660492
## 49.57143 4.606545 5.682959
## 49.71429 4.617278 5.698022
## 49.85714 4.625941 5.710869
## 50.00000 4.633969 5.723028
## 50.14286 4.641789 5.734952
## 50.28571 4.649526 5.746775
## 50.42857 4.657220 5.758537
## 50.57143 4.664880 5.770251
## 50.71429 4.672511 5.781921
## 50.85714 4.680113 5.793549
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 2.648742 2.678070 2.498315 2.625344 2.731472 2.743976 2.459315 2.555848
##  [9] 2.555848 2.555848 2.555848 2.555848 2.555848 2.555848
## 
## $愛媛県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                  80%        95%
## 49.00000  0.33382346 -0.8916206
## 49.14286 -0.05891859 -1.5077930
## 49.28571 -0.60383986 -2.2460216
## 49.42857 -0.80330421 -2.6183207
## 49.57143 -0.99517306 -2.9679398
## 49.71429 -1.25854063 -3.3773451
## 49.85714 -1.80124821 -4.0566543
## 50.00000 -1.84734415 -4.1782535
## 50.14286 -2.01870337 -4.4403248
## 50.28571 -2.18387134 -4.6929274
## 50.42857 -2.34347426 -4.9370190
## 50.57143 -2.49803939 -5.1734060
## 50.71429 -2.64801566 -5.4027749
## 50.85714 -2.79378901 -5.6257160
## 
## $愛媛県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 4.963661  6.189105
## 49.14286 5.415059  6.863934
## 49.28571 5.600470  7.242652
## 49.42857 6.053991  7.869008
## 49.57143 6.458117  8.430884
## 49.71429 6.746493  8.865298
## 49.85714 6.719879  8.975285
## 50.00000 6.959041  9.289950
## 50.14286 7.130400  9.552022
## 50.28571 7.295568  9.804624
## 50.42857 7.455171 10.048716
## 50.57143 7.609736 10.285103
## 50.71429 7.759712 10.514472
## 50.85714 7.905486 10.737413
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 20.52457 18.84948 29.93232 21.07753 22.11508 26.67486 21.96198 23.34375
##  [9] 25.03020 22.66881 23.73177 24.24594 23.12844 23.80737
## 
## $高知県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 17.94316 16.57665
## 49.14286 16.16464 14.74337
## 49.28571 27.13531 25.65466
## 49.42857 17.70460 15.91908
## 49.57143 18.63080 16.78633
## 49.71429 23.02275 21.08944
## 49.85714 18.00465 15.90976
## 50.00000 19.26107 17.09983
## 50.14286 20.77426 18.52130
## 50.28571 18.20468 15.84151
## 50.42857 19.13533 16.70213
## 50.57143 19.48773 16.96889
## 50.71429 18.20473 15.59828
## 50.85714 18.75111 16.07448
## 
## $高知県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 23.10598 24.47250
## 49.14286 21.53433 22.95560
## 49.28571 32.72934 34.20999
## 49.42857 24.45046 26.23598
## 49.57143 25.59936 27.44383
## 49.71429 30.32697 32.26028
## 49.85714 25.91931 28.01420
## 50.00000 27.42642 29.58766
## 50.14286 29.28613 31.53909
## 50.28571 27.13294 29.49610
## 50.42857 28.32820 30.76140
## 50.57143 29.00415 31.52299
## 50.71429 28.05215 30.65860
## 50.85714 28.86364 31.54026
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1]  78.39145  91.67500 107.06804  99.85765  94.40462  90.40716  93.78463
##  [8]  91.86097  96.37137 105.65890  99.01988  95.32125  93.54223  97.88622
## 
## $福岡県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 63.98798 56.36325
## 49.14286 74.38276 65.22880
## 49.28571 88.51494 78.69352
## 49.42857 80.48931 70.23634
## 49.57143 74.24639 63.57526
## 49.71429 69.28830 58.10865
## 49.85714 71.45070 59.62782
## 50.00000 66.93712 53.74324
## 50.14286 69.19028 54.80148
## 50.28571 76.42203 60.94496
## 50.42857 67.85527 51.35772
## 50.57143 62.31746 44.84631
## 50.71429 58.76855 40.36048
## 50.85714 61.40387 42.09128
## 
## $福岡県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%      95%
## 49.00000  92.79491 100.4196
## 49.14286 108.96725 118.1212
## 49.28571 125.62114 135.4426
## 49.42857 119.22598 129.4790
## 49.57143 114.56285 125.2340
## 49.71429 111.52603 122.7057
## 49.85714 116.11857 127.9414
## 50.00000 116.78481 129.9787
## 50.14286 123.55246 137.9413
## 50.28571 134.89578 150.3728
## 50.42857 130.18450 146.6821
## 50.57143 128.32504 145.7962
## 50.71429 128.31591 146.7240
## 50.85714 134.36857 153.6812
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 5.211006 5.211006 5.211006 5.211006 5.211006 5.211006 5.211006 5.211006
##  [9] 5.211006 5.211006 5.211006 5.211006 5.211006 5.211006
## 
## $佐賀県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%       95%
## 49.00000 2.965176 1.7763048
## 49.14286 2.895071 1.6690882
## 49.28571 2.827026 1.5650232
## 49.42857 2.760871 1.4638472
## 49.57143 2.696455 1.3653320
## 49.71429 2.633649 1.2692783
## 49.85714 2.572337 1.1755102
## 50.00000 2.512418 1.0838720
## 50.14286 2.453801 0.9942247
## 50.28571 2.396404 0.9064441
## 50.42857 2.340155 0.8204181
## 50.57143 2.284987 0.7360455
## 50.71429 2.230840 0.6532346
## 50.85714 2.177659 0.5719016
## 
## $佐賀県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 7.456837 8.645708
## 49.14286 7.526942 8.752924
## 49.28571 7.594987 8.856989
## 49.42857 7.661142 8.958166
## 49.57143 7.725558 9.056681
## 49.71429 7.788364 9.152734
## 49.85714 7.849675 9.246503
## 50.00000 7.909594 9.338141
## 50.14286 7.968212 9.427788
## 50.28571 8.025608 9.515569
## 50.42857 8.081858 9.601595
## 50.57143 8.137026 9.685967
## 50.71429 8.191173 9.768778
## 50.85714 8.244354 9.850111
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1]  8.481183 11.274881  9.844421 10.946340 11.429178 10.141435 10.704205
##  [8] 10.505597 11.985098 11.282337 11.851945 11.979870 11.341120 11.661745
## 
## $長崎県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 6.216947 5.018332
## 49.14286 8.753554 7.418844
## 49.28571 6.804408 5.195122
## 49.42857 7.600352 5.829093
## 49.57143 7.741123 5.788784
## 49.71429 6.170120 4.067833
## 49.85714 6.454493 4.204831
## 50.00000 5.839661 3.369663
## 50.14286 7.013816 4.382178
## 50.28571 5.987331 3.184323
## 50.42857 6.269525 3.314371
## 50.57143 6.115611 3.011259
## 50.71429 5.212127 1.967634
## 50.85714 5.276950 1.897044
## 
## $長崎県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 10.74542 11.94403
## 49.14286 13.79621 15.13092
## 49.28571 12.88443 14.49372
## 49.42857 14.29233 16.06359
## 49.57143 15.11723 17.06957
## 49.71429 14.11275 16.21504
## 49.85714 14.95392 17.20358
## 50.00000 15.17153 17.64153
## 50.14286 16.95638 19.58802
## 50.28571 16.57734 19.38035
## 50.42857 17.43437 20.38952
## 50.57143 17.84413 20.94848
## 50.71429 17.47011 20.71461
## 50.85714 18.04654 21.42644
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 29.87154 29.76489 29.96594 29.96594 29.96594 29.96594 29.96594 29.96594
##  [9] 29.96594 29.96594 29.96594 29.96594 29.96594 29.96594
## 
## $熊本県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 24.91786 22.29554
## 49.14286 23.78822 20.62436
## 49.28571 23.72313 20.41838
## 49.42857 23.30267 19.77536
## 49.57143 22.90722 19.17057
## 49.71429 22.53278 18.59791
## 49.85714 22.17632 18.05274
## 50.00000 21.83547 17.53146
## 50.14286 21.50834 17.03116
## 50.28571 21.19341 16.54951
## 50.42857 20.88939 16.08456
## 50.57143 20.59524 15.63469
## 50.71429 20.31004 15.19851
## 50.85714 20.03302 14.77486
## 
## $熊本県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 34.82523 37.44755
## 49.14286 35.74157 38.90543
## 49.28571 36.20875 39.51349
## 49.42857 36.62920 40.15651
## 49.57143 37.02465 40.76130
## 49.71429 37.39909 41.33396
## 49.85714 37.75555 41.87913
## 50.00000 38.09640 42.40041
## 50.14286 38.42353 42.90071
## 50.28571 38.73846 43.38236
## 50.42857 39.04248 43.84731
## 50.57143 39.33663 44.29718
## 50.71429 39.62183 44.73336
## 50.85714 39.89885 45.15701
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 12.44839 13.05136 13.14603 14.46604 15.12757 14.49903 13.71394 15.20098
##  [9] 15.86361 15.94887 14.56642 13.24944 12.68727 13.62760
## 
## $大分県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                80%       95%
## 49.00000  9.964944  8.650288
## 49.14286 10.092617  8.526353
## 49.28571  9.950754  8.259278
## 49.42857 11.111307  9.335417
## 49.57143 11.644353  9.800452
## 49.71429 10.901824  8.997579
## 49.85714 10.010539  8.050074
## 50.00000 11.442757  9.453272
## 50.14286 12.037439 10.011988
## 50.28571 12.049164  9.984784
## 50.42857 10.591374  8.487111
## 50.57143  9.199004  7.054833
## 50.71429  8.562163  6.378465
## 50.85714  9.428855  7.206175
## 
## $大分県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 14.93183 16.24649
## 49.14286 16.01010 17.57637
## 49.28571 16.34130 18.03278
## 49.42857 17.82078 19.59667
## 49.57143 18.61078 20.45468
## 49.71429 18.09623 20.00048
## 49.85714 17.41735 19.37782
## 50.00000 18.95921 20.94870
## 50.14286 19.68977 21.71522
## 50.28571 19.84858 21.91296
## 50.42857 18.54147 20.64573
## 50.57143 17.29988 19.44405
## 50.71429 16.81237 18.99607
## 50.85714 17.82634 20.04902
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 8.109768 7.806931 7.806931 7.806931 7.806931 7.806931 7.806931 7.806931
##  [9] 7.806931 7.806931 7.806931 7.806931 7.806931 7.806931
## 
## $宮崎県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%         95%
## 49.00000 4.996462  3.34837728
## 49.14286 4.526084  2.78930795
## 49.28571 4.219613  2.32060199
## 49.42857 3.937340  1.88890168
## 49.57143 3.674302  1.48661962
## 49.71429 3.427032  1.10845349
## 49.85714 3.192995  0.75052498
## 50.00000 2.970270  0.40989572
## 50.14286 2.757359  0.08427628
## 50.28571 2.553068 -0.22815835
## 50.42857 2.356430 -0.52889084
## 50.57143 2.166643 -0.81914521
## 50.71429 1.983037 -1.09994588
## 50.85714 1.805046 -1.37216050
## 
## $宮崎県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 11.22307 12.87116
## 49.14286 11.08778 12.82455
## 49.28571 11.39425 13.29326
## 49.42857 11.67652 13.72496
## 49.57143 11.93956 14.12724
## 49.71429 12.18683 14.50541
## 49.85714 12.42087 14.86334
## 50.00000 12.64359 15.20397
## 50.14286 12.85650 15.52959
## 50.28571 13.06079 15.84202
## 50.42857 13.25743 16.14275
## 50.57143 13.44722 16.43301
## 50.71429 13.63082 16.71381
## 50.85714 13.80882 16.98602
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 12.50537 13.00140 12.65038 12.90550 12.90550 12.90550 12.90550 12.90550
##  [9] 12.90550 12.90550 12.90550 12.90550 12.90550 12.90550
## 
## $鹿児島県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 7.936301 5.517581
## 49.14286 7.472219 4.545247
## 49.28571 6.748860 3.624784
## 49.42857 6.793830 3.558510
## 49.57143 6.738106 3.473287
## 49.71429 6.682881 3.388828
## 49.85714 6.628142 3.305111
## 50.00000 6.573876 3.222118
## 50.14286 6.520071 3.139831
## 50.28571 6.466715 3.058231
## 50.42857 6.413798 2.977301
## 50.57143 6.361310 2.897026
## 50.71429 6.309238 2.817390
## 50.85714 6.257575 2.738378
## 
## $鹿児島県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 17.07444 19.49316
## 49.14286 18.53058 21.45755
## 49.28571 18.55190 21.67598
## 49.42857 19.01716 22.25248
## 49.57143 19.07289 22.33770
## 49.71429 19.12811 22.42216
## 49.85714 19.18285 22.50588
## 50.00000 19.23712 22.58887
## 50.14286 19.29092 22.67116
## 50.28571 19.34428 22.75276
## 50.42857 19.39719 22.83369
## 50.57143 19.44968 22.91397
## 50.71429 19.50175 22.99360
## 50.85714 19.55342 23.07261
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##  [1] 28.09364 29.69360 25.55521 31.90529 22.75541 24.36221 23.54593 24.75256
##  [9] 24.75256 24.75256 24.75256 24.75256 24.75256 24.75256
## 
## $沖縄県$lower
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##                  80%        95%
## 49.00000 15.34678396   8.599005
## 49.14286 15.94595268   8.668389
## 49.28571 10.87484266   3.103527
## 49.42857 16.34802560   8.112507
## 49.57143  6.36810214  -2.306815
## 49.71429  7.18492547  -1.908182
## 49.85714  5.61342314  -3.879470
## 50.00000  4.80996632  -5.747003
## 50.14286  3.74811122  -7.370970
## 50.28571  2.73741307  -8.916700
## 50.42857  1.77112142 -10.394515
## 50.57143  0.84385146 -11.812652
## 50.71429 -0.04877401 -13.177805
## 50.85714 -0.91037033 -14.495503
## 
## $沖縄県$upper
## Time Series:
## Start = c(49, 1) 
## End = c(50, 7) 
## Frequency = 7 
##               80%      95%
## 49.00000 40.84050 47.58828
## 49.14286 43.44124 50.71881
## 49.28571 40.23557 48.00689
## 49.42857 47.46256 55.69808
## 49.57143 39.14272 47.81764
## 49.71429 41.53950 50.63261
## 49.85714 41.47843 50.97132
## 50.00000 44.69515 55.25212
## 50.14286 45.75700 56.87608
## 50.28571 46.76770 58.42181
## 50.42857 47.73399 59.89963
## 50.57143 48.66126 61.31776
## 50.71429 49.55389 62.68292
## 50.85714 50.41548 64.00062